Climate Change and AI Berlin Brandenburg
In the 21st century, climate change and digitalization are megatrends, and their development will be decisive for humanity’s future. Climate change (CC) will determine the livability of the planet, while digitalization, artificial intelligence (AI) and machine learning will shape the way we work and live.
The Climate Change & Artificial Intelligence community in Berlin
Our Mission: Build a community
We provide an interface between the artificial intelligence and climate change communities in the Berlin Brandenburg region, offering joint workshops, developing joint strategies and establishing new collaborations.
Leading researchers at the intersection of CC & AI are working in diverse institutions in the Berlin-Brandenburg region. A wide range of AI experts and world-leading specialists are organized in the Berlin Institute for the Foundations of Learning and Data (BIFOLD) and the German Research Center for Artificial Intelligence (DFKI). Climate change research is similarly recognized internationally, spearheaded by the Potsdam Institute of Climate Impact Research (PIK) as well as the Berlin Mercator Research Institute on Global Commons and Climate Change (MCC), and including investigators and centers from a wide array of Berlin and Brandenburg institutions.
Our goal: Deliver AI solutions for climate change (on the levels of governance, pathways, tools)
- We will organize joint seminars of AI and climate change research in Berlin and Brandenburg. These will be convened online bi-monthly, as well as in-person twice a year. In these seminars, researchers will present their analyses and seek feedback both on method and scope. This exchange will accelerate skills and knowledge and is envisaged to also foster collaboration between research groups and different scientific fields.
- We will establish a strategic base for furthering research and academic excellence on climate change & AI and AI-based privacy preserving climate governance.
- We will advance strategic research projects that encompass several PIs and working groups. This comprises existing and new research projects.
How: Workshops and Lectures
December 6th – 8th, 2021
Workshop on climate change and artificial intelligence
Blossin (Brandenburg, Germany)
December 13th - Dec 20th
Visit Marta Gonzalez
UC Berkeley College of Environmental Design
Prof. Dr. Felix Creutzig (Coordinator)
Prof. Dr. Felix Creutzig is the head of the working group Land Use, Infrastructures and Transport at MCC and the Chair of Sustainability Economics of Human Settlements at Technische Universität Berlin. His research focuses conceptualizing and quantifying GHG emissions of cities world-wide, developing an AI-based framework for agile low-carbon urban planning. He is currently coordinating lead author of the IPCC’s 6th Assessment Report.
Prof. Dr. Lynn Kaack (Coordinator)
Lynn Kaack is Assistant Professor of Computer Science and Public Policy at the Hertie School, and a co-founder and chair of the organization Climate Change AI. Her research focuses on methods from statistics and machine learning to inform climate mitigation policy across the energy sector, and she also works on climate-related AI policy.
Prof. Dr. Niklas Boers
Niklas Boers is a physicist and mathematicial by training and has worked in methodological developments for Earth system science for ten years. He is currently Professor in Earth System Modelling at TU Munich and leads the Future Lab for Artificial Intelligence in the Anthropocene at the Potsdam Institute for Climate Impact Research. His current research interests focus on the development of hybrid approaches combining physical and Machine Learning methods to model mostly nonlinear behavior in the Earth system.
Prof. Dr. Begüm Demir
Begüm Demir is a Professor and the founder head of the Remote Sensing Image Analysis (RSiM) group at the Faculty of Electrical Engineering and Computer Science, TU Berlin and the head of the Big Data Analytics for Earth Observation research group at the Berlin Institute for the Foundations of Learning and Data (BIFOLD). She performs research in the field of processing and analysis of large-scale Earth observation data acquired by airborne and satellite-borne systems.
Prof. Dr. Slava Jankin
Slava Jankin is Professor of Data Science and Public Policy at the Hertie School. He is the Director of the Hertie School Data Science Lab. His research and teaching is primarily in the field of natural language processing and machine learning focusing on health implications of climate change.
Prof. Dr. Sebastian Möller
Sebastian Möller is full professor for the subject area Quality and Usability at TU Berlin, and leads the research department on Speech and Language Technology at DFKI Berlin. He is interested in ML-based information extraction from text and speech, as well as in analyzing human behavior with respect to climate (mis-) information.
Dr. Peter-Paul Pichler
Peter-Paul Pichler, PhD, was trained as a computer scientist and received his PhD in the field of complex adaptive systems. He has been working at the Potsdam Institute for Climate Impact Research since 2009 and is currently deputy lead of the FutureLab Social Metabolism and Impacts. As a social ecologist, his research focuses on the analysis of different aspects of social resource use. His current research concentrates mainly on energy use and greenhouse gas emissions in a distributional, regional, and sectoral context with a methodological focus on multi-regional input-output models, complex network analysis, and other quantitative empirical methods of data analysis. He is also interested in reproducible research and open science.
Prof. Dr. Jakob Runge
Jakob Runge heads the Climate Informatics group at German Aerospace Center’s Institute of Data Science in Jena since 2017 and is guest professor of computer science at TU Berlin since 2021 through the ERC Starting Grant CausalEarth. He studied physics at Humboldt University Berlin and obtained his PhD at the Potsdam Institute for Climate Impact Research in 2014.
Picture © Mathematisches Forschungsinstitut Oberwolfach
Prof. Dr. Philipp Staab
Philipp Staab is Professor of „Sociology of the Future of Work“ at Humboldt-Universität Berlin and at Einstein Center Digital Future (ECDF). As a sociologist he works on topics of technology, work, political economy, social inequality and social adaptation.
Dr. Quentin Lejeune
Quentin has a broad scientific background related to climate changes issues, especially climate impacts, interactions between land use and climate and climate services. He is leading in our science team on land use and climate services, coordinates the European JPI-AXIS project LAMACLIMA, and is leading the work package on climate services products in the H2020 project PROVIDE. His work is diverse and consists in coordinating research but also stakeholder engagement activities in a close cooperation with other team members and partner institutions. More specifically, he is in charge of the co-production of climate impact information and of online tools to make it accessible, in collaboration with scientists and stakeholders. In addition, he supports the science team on diverse issues related to climate physics and climate impacts for example during climate negotiations, and through media outreach activities targeted at a wider public. Before joining Climate Analytics, Quentin was conducting academic research, focusing on the consequences of past and future land-cover changes on regional climate, and the evaluation of related processes in climate models. He conducted his PhD in Climate Science at ETH Zürich, where he later worked as a postdoc and where he is still affiliated. He has also authored a number of peer-reviewed articles.
Dr. Carl-Friedrich Schleussner
Dr. Carl-Friedrich Schleussner is the Head of Climate Science at Climate Analytics and Group Leader at Humboldt University Berlin. He has longstanding expertise in climate modelling and climate impact science, multi-year experience in providing scientific advice at the climate-policy interface including the UNFCCC process. A climate physicist by training, he has received a PhD with distinction at the Potsdam Institute for Climate Impact Research and his publication record spans a wide range from climate extreme and climate impact projections including water availability and food production to tipping elements and societal implications of climate change. He has published more than 60 peer reviewed publications, book chapters and reports and his work has been covered in major news outlets around the globe.
Dr. Leonie Wenz
Leonie Wenz leads the working group “Data-based analysis of climate decisions” at the Potsdam Institute for Climate Impact Research (PIK) and acts as deputy head of PIK’s research department “Complexity Science”. With her team, she employs a variety of data-analytic methods, ranging from statistics, econometrics, and machine learning to numerical modelling, to uncover how climate affects human well-being and economic development. A mathematician by training, Leonie obtained her PhD from University of Potsdam with a dissertation at the intersection of Climate Physics and Environmental Economics. She worked as a Postdoc at the Department of Agricultural and Resource Economics at UC Berkeley. Her work has appeared in, among others, Nature Climate Change, PNAS, and JEEM and has informed e.g. the German parliament and central banks.
Dr. Anna Almosova
Anna Almosova received a PhD in Economics from HU Berlin and is currently an ECDF junior professor at TU Berlin. She works on interdisciplinary projects on the edge of macroeconomics and computer science such as forecasting (machine learning and XAI), agent behaviour and decisions making (reinforcement learning) and private and digital currencies.
Nikola Milojevic-Dupont is a PhD candidate at MCC Berlin and the Technical University Berlin, and the Chair of the Content Committee at Climate Change AI. He works on sustainable urban planning, with a particular focus on applying machine learning techniques to support data-driven public policies in urban areas.
Prof. Dr. Jan Minx
Jan Minx is Head of the working group Applied Sustainability Science and the Priestley Chair for Climate Change and Public Policy at the University of Leeds. His research focuses on using data science methods for synthesizing the exponentially growing body of climate change research and using natural language processing for understanding influence, public perception and political economy in the context of climate policies.
Prof. Dr. Rebecca D. Frank
Rebecca D. Frank is the Assistant Professor for Information Management at the Berlin School of Library and Information Science at the Humboldt-Universität zu Berlin and the Einstein Center Digital Future (ECDF). Her research examines the social construction of risk in trustworthy digital repository audit and certification. She also conducts research in the areas of open data, digital preservation, digital curation, and data reuse, focusing on social and ethical barriers that limit or prevent the preservation, sharing, and reuse of digital information.
Stefanie Kunkel is an economist working in the project “Digitalization and Sustainability Transformations” at the Institute for Advanced Sustainability Studies e.V. (IASS) in Potsdam. In the past, she has worked and done research in various contexts in the field of international environmental policy and economics, including in the United Nations Environment Programme in Geneva and in the European Parliament in Brussels. Her research focuses on the implications of digitalization for ecologically sustainable value chains in a global context, where she puts a focus on digitalization of industry in China. Stefanie is particularly interested in the question of how artificial intelligence can be shaped and designed politically and socially such that its use contributes to the well-being of people and the environment.
Abdulla Ghani is a Junior professor at the Interdisziplinäres Zentrum für Modellierung und Simulation (IMoS) at TU Berlin and heads the Data Analysis and Modeling of Turbulent Flows group. He develops methods combining high-performance computing and data-driven models for combustion processes with zero-carbon and noise emissions.